Join Rituparna Ghosh from Wipro as she will explore the impact of Gen AI on software development and testers’ roles. 
Discover how tools like GitHub Copilot are enhancing productivity and the tester experience, providing insights on future tester routines and cognitive factors affecting their work.
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The belief that GenAI will elevate testers’ roles may be overly optimistic. Automation could instead commoditize testing, leading organizations to undervalue human judgment in favor of AI.
Hi there,
If you couldn’t catch the session live, don’t worry! You can watch the recording here:
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Here are some unanswered questions that were asked in the session:
As testers increasingly depend on GenAI, there is a risk that their own skills will atrophy. Critical thinking, problem-solving, and domain-specific knowledge might diminish as testers defer to AI-driven tools. How will you address this?
How is AI transforming the traditional roles and responsibilities of testers?
How do you envision AI enhancing our shift-left testing strategy? Are there specific areas where AI could provide the most value?
How can AI be integrated?
How can AI be implemented?
Can you demonstrate how AI can be used to predict potential test failures or bottlenecks, and how this can elevate the role of a tester by enabling more proactive and strategic testing?
What are the best methods to upskill to become current (and even future-proofed) in AI-in-testing skillsets?
What are the greatest opportunities in testing and AI that people are currently overlooking?
How can AI be integrated into software testing to improve efficiency and accuracy?
How do we compare the successful outcome of TestMu and what suggestions do you guys have to improve it more?
How could we use AI tools to improve testing on legacy systems that only do regression tests and validation of small features?
Don’t believe everything you hear. Understand the subject and form your opinions.
How do you see the future of AI evolving within the testing industry, and what should testers prepare for?
How can manual testers uplevel their skills in the new AI era?
How AI can help in traditional testing?
How will AI continue to evolve, and what new opportunities or challenges might arise for testers in the next few years?
How can AI-driven tools enhance the efficiency and accuracy of manual testing processes without replacing the need for human intuition and critical thinking in QA?
